基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例

doi:10.3864/j.issn.0578-1752.2020.24.004

摘要/Abstract

摘要：

【目的】探究黄河三角洲麦田土壤盐分准确高效的遥感提取方法,掌握土壤盐渍化程度与分布。【方法】以垦利区为研究区,均匀布设冬小麦种植区样点77个,同时设置代表性试验区2个,网格布设样点99个,实测采集麦田土壤表层盐分数据及试验区无人机多光谱图像。筛选红、绿、红边、近红4个波段及SI、NDVI、DVI、RVI、GRVI 5个光谱指数中的敏感光谱参量,采用逐步回归、偏最小二乘法、BP神经网络及SVM支持向量机4种方法建立土壤盐分估测模型,使用波段比值均值法得到Sentinel-2A卫星影像相应波段的修正系数,进而将筛选的土壤盐分估测模型转换为基于卫星影像的反演模型,经麦区实测样点数据验证,得到最佳的麦区土壤盐分反演模型,实现试验区和研究区2个尺度的麦田土壤盐分反演。【结果】无人机4个波段及光谱指数NVDI、RVI、SI与土壤盐分含量相关性显著,4种建模方法的13个模型中,以NDVI、RVI、SI建立的4个指数模型的建模及验证R²均优于其他模型;对4个模型进行升尺度修正及验证,效果最佳的反演模型为偏最小二乘法光谱指数模型：Y=-9.4774×NDVI₁+0.4794×RVI₁+3.0747×SI₁+5.0604,验证R²为0.513,RMSE为1.379;利用该模型反演得到了试验区及整个研究区麦田土壤盐分等级分布图,结合实测插值及调查结果,证明反演模型及空间分布结果准确、可靠。【结论】本研究构建了卫星、无人机一体化的滨海麦区土壤盐分反演模型,对滨海盐渍区农作物的生产管理有积极参考价值。

关键词: 冬小麦, 无人机, Sentinel-2A卫星, 土壤盐分, 反演模型

Abstract:

【Objective】 The purpose of this paper was to explore an accurate and efficient remote sensing method for soil salinity extraction of wheat field in the Yellow River Delta, and obtain the degree and distribution of soil salinization of wheat fields.【Method】This study took Kenli District as the research area, and set 77 sample points in winter wheat growing area evenly. At the same time, two representative test areas and 99 grid sample points were set, and the surface soil salinity data in wheat field and the multi-spectral images of UAV in the test area were collected. The sensitive spectral parameters were screened from four spectral bands (red, green, red edge, and near-infrared) and five spectral indexes (SI, NDVI, DVI, RVI, and GRVI). Stepwise regression, partial least squares, BP neural network and support vector machine methods were used to establish models for predicting the soil salinity, and the band ratio mean method was used to obtain the correction coefficient of the corresponding band of sentinel-2A satellite image. And then the selected soil salinity estimation model was converted into an inversion model based on satellite image. After using the data from the wheat field sample points to verify the models, the best soil salinity inversion model in wheat field was selected, and two scales of soil salinity inversion are realized in the test areas and the research area.【Result】The results showed that the four bands of UAV and the spectral indexes NVDI, RVI and SI were significantly correlated with soil salinity. Among the 13 models of the four modeling methods, the four index models established by NDVI, RVI and SI were better than the other models in modeling and verifying R ²; The best inversion model was the spectral index model obtained by partial least square method: Y=-9.4774×NDVI₁+ 0.4794×RVI₁+ 3.0747×SI₁+ 5.0604, and the accuracy R² was 0.513 and RMSE was 1.379. By using this model, the soil salinity distribution map of the test area and the whole wheat area was obtained. Combined with the measured interpolation and the survey, the inversion model and spatial distribution results were proved to be accurate and reliable. 【Conclusion】In this study, the soil salinity inversion model of the coastal wheat area based on the integration of satellite and UAV was constructed, which had positive reference value for the production and management of crops in the coastal saline area.

Key words: winter wheat, unmanned aerial vehicle, sentinel-2A satellite, soil salinity, inversion model

奚雪,赵庚星,高鹏,崔昆,李涛. 基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例[J]. 中国农业科学, 2020, 53(24): 5005-5016.

XI Xue,ZHAO GengXing,GAO Peng,CUI Kun,LI Tao. Inversion of Soil Salinity in Coastal Winter Wheat Growing Area Based on Sentinel Satellite and Unmanned Aerial Vehicle Multi-Spectrum— A Case Study in Kenli District of the Yellow River Delta[J]. Scientia Agricultura Sinica, 2020, 53(24): 5005-5016.

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光谱指数 Spectral index	计算公式 Computational formula	参考文献 Reference
归一化植被指数NDVI	(b_NIR-b_R)/( b_NIR+b_R)	[32]
比值植被指数RVI	b_NIR/b_R	[32]
差值植被指数DVI	b_NIR-b_R	[32]
绿波段比值植被指数GRVI	b_NIR/b_G	[32]
盐分指数SI	$\sqrt{bG\times bR}$	[33]

土壤盐渍化等级 Soil salinity grade	非盐渍化 Non-salinized	轻度盐渍化 Mild salinized	中度盐渍化 Moderate salinized	重度盐渍化 Severe salinized	盐土 Salinized soil
分级标准 Grade standard (g·kg^-1)	SS<1	1≤SS<2	2≤SS<4	4≤SS<6	SS>6
等级 Grade	1	2	3	4	5

变量 Variable	SS	G	R	REG	NIR
SS	1
G	0.677**	1
R	0.667**	0.506**	1
REG	0.616**	0.431**	0.578**	1
NIR	-0.671**	-0.561**	-0.449**	-0.486**	1

变量 Variable	SS	SI	NDVI	DVI	RVI	GRVI
SS	1
SI	0.770**	1
NDVI	-0.787**	-0.808**	1
DVI	-0.780**	-0.768**	0.953**	1
RVI	-0.560**	-0.696**	0.890**	0.844**	1
GRVI	-0.432**	-0.690**	0.468**	0.557**	0.398**	1

建模方法 Modeling approach	光谱参量 Spectral parameter	估测模型 Estimating model	建模精度 Modeling accuracy		验证精度 Verification accuracy
建模方法 Modeling approach	光谱参量 Spectral parameter	估测模型 Estimating model	R²	RMSE	R²	RMSE
逐步回归 Stepwise regression	b_G	Y=18.609×b_G+0.169	0.458	1.267	0.401	0.882
	b_G,b_R	Y=12.546×b_G+19.044×b_R-1.120	0.599	1.089	0.600	0.729
	b_G,b_R,b_NIR	Y=8.360×b_G+15.775×b_R-9.912×b_NIR +3.116	0.673	0.983	0.650	0.847
	b_G,b_R,b_NIR,b_REG	Y=7.988×b_G+12.282×b_R-8.525×b_NIR+6.979×b_REG+2.101	0.694	0.951	0.648	0.771
	NDVI	Y=-7.507×NDVI+6.308	0.620	1.061	0.668	0.685
	NDVI,RVI	Y=-13.261×NDVI+0.69×RVI+6.734	0.715	0.918	0.692	0.897
	NDVI,RVI,SI	Y=-10.287×NDVI+0.651×RVI+13.486×SI+3.843	0.756	0.850	0.710	0.907
偏最小二乘法 Partial least squares	b_G,b_R,b_NIR,b_REG	Y=6.021×b_G+6.5986×b_R+6.2650×b_NIR-4.1737×b_REG+1.3260	0.689	1.114	0.719	1.177
偏最小二乘法 Partial least squares	NDVI,RVI,SI	Y=-9.4774×NDVI+0.4794×RVI+3.0747×SI+5.0604	0.734	0.954	0.784	0.769
BP神经网络 The BP neural network	b_G,b_R,b_NIR,b_REG	—	—	—	0.714	0.893
BP神经网络 The BP neural network	NDVI,RVI,SI	—	—	—	0.753	0.993
支持向量机 Support vector machine	b_G,b_R,b_NIR,b_REG	—	0.804	0.590	0.467	0.473
支持向量机 Support vector machine	NDVI,RVI,SI	—	0.835	0.353	0.640	0.512